Existing methods [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]} have paid little attention to the issue as mentioned above and mainly learned the grounding task under specific views. The vision-language correspondence learned in this way can easily fail once the view changes. An intuitive solution to alleviating the problem is to add augmented rotation data under different views during training. However, as we tried and tested (see Tab. REF  in our experimental section), such a straightforward method does not bring the improvement. This result can be understood, i.e., although novel views of the 3D scene can be augmented in the training stage, visual features under specific views are not accurately aligned with the position information embedded in the language description. An alternative is to ask the model to predict the view attached to the language query, say, the speaker's view, and then align the query with the visual features under the predicted view. However, this will incur a lot more costs for view prediction, and if the model predicts the wrong view, the alignment between the two modalities will inevitably be damaged. Experiments provided by LanguageRefer [2]} confirmed this point, i.e., even if they manually correct the view of all training data to the speaker's view, the performance of the model on unseen test data with view discrepancy still cannot be improved. Therefore, in this paper, we turn to the exploration of learning better multi-modal representations for 3D visual grounding, which are robust to view changes, instead of augmenting view data or predicting views.
